A community benchmark for Qwen3.8-Flash-Next-oQ4e-mtp adds a concrete data point to the debate on local Apple Silicon inference. On a Mac with M4 Max the variant reaches 45 tokens/s; on an M2 Ultra it sits at 25 tokens/s. The comparison cited in the source is just as interesting: Qwen 3.8 Flash Next offers speed similar to Qwen3.8 27B on the same hardware category.

The key point is not the absolute number but the relationship between the two platforms. The M4 Max beats the M2 Ultra even though the latter is a larger chip and, on paper, has more resources. In sequential generation workloads like LLM inference, throughput does not scale with core count; it depends mainly on how fast the model weights can be moved through memory. The data suggests that the M4 Max's newer architecture—or its different balance of CPU, GPU, and memory bandwidth—has a bigger effect than raw silicon area. That's a structural lesson for anyone planning on-premise deployment on Apple hardware: the most expensive Mac does not guarantee the lowest latency.

For self-hosted evaluation, this benchmark has immediate practical value. Throughput of 45 tokens/s is enough for fluid interaction in many business use cases, provided the context length does not grow too much. But the gap between the two machines also exposes a risk: chasing larger hardware can become a TCO trap. A more expensive Mac with more GPU cores does not always pay off in tokens/s; the choice should focus on memory bandwidth, SoC generation, and model format, not peak compute.

The oQ4e-mtp suffix identifies a specific variant. The relevant point is not decoding the suffix but understanding that in an Apple Silicon ecosystem with unified memory, model format and quantization have a huge impact: tokens/s depend on how quickly weights are read. An optimized variant can make a model practical that would otherwise require too much unified memory.

The similar speed to Qwen3.8 27B on Apple Silicon is the real signal: if a smaller or optimized variant offers the same fluidity as a larger model, the savings in memory and TCO can be significant. Whether response quality holds up remains an open question, but the source does not provide that evidence. The fact that these numbers circulate on a community benchmark rather than official documentation also says something about the market: Apple Silicon optimization is still largely a community-driven space. Companies that want to avoid the cloud and keep data on-premise need independent references, but they also have to accept that performance depends on combinations of model, format, and hardware that are hard to predict without testing. For those evaluating on-premise deployment, AI-RADAR publishes analytical frameworks on /llm-onpremise to assess these trade-offs.

In short, the benchmark does not crown a single chip. It shifts attention to a variable that TCO spreadsheets often miss: the fit between silicon generation, memory bandwidth, and optimized model format. On Apple Silicon, the largest Mac is not automatically the fastest local node.